Literature DB >> 31383263

Total Laboratory Automation: What Is Gained, What Is Lost, and Who Can Afford It?

Richard B Thomson1, Erin McElvania2.   

Abstract

The first clinical microbiology laboratory in the United States adopted total automation for bacteriology processing in 2014. Since then, others have followed with installation of either the BD Kiestra TLA or the Copan WASPLab. This article discusses commercially available automated systems in the United States; why automation is needed; and quality improvements, efficiency, and cost savings associated with automation. After learning how these systems are used, gains and losses experienced, and how one can afford the most expensive equipment ever purchased for clinical microbiology laboratories, the question is, how can one afford not to purchase one of these microbiology automation systems?
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  BD Kiestra TLA; Copan WASPLab; Microbiology automation; Microbiology cost savings; Microbiology efficiency; Microbiology quality

Year:  2019        PMID: 31383263     DOI: 10.1016/j.cll.2019.05.002

Source DB:  PubMed          Journal:  Clin Lab Med        ISSN: 0272-2712            Impact factor:   1.935


  5 in total

1.  Machine Learning Takes Laboratory Automation to the Next Level.

Authors:  Bradley A Ford; Erin McElvania
Journal:  J Clin Microbiol       Date:  2020-03-25       Impact factor: 5.948

Review 2.  Automation in the Life Science Research Laboratory.

Authors:  Ian Holland; Jamie A Davies
Journal:  Front Bioeng Biotechnol       Date:  2020-11-13

3.  Total Laboratory Automation and Three Shifts Reduce Turnaround Time of Cerebrospinal Fluid Culture Results in the Chinese Clinical Microbiology Laboratory.

Authors:  Weili Zhang; Siying Wu; Jin Deng; Quanfeng Liao; Ya Liu; Li Xiong; Ling Shu; Yu Yuan; Yuling Xiao; Ying Ma; Mei Kang; Dongdong Li; Yi Xie
Journal:  Front Cell Infect Microbiol       Date:  2021-12-02       Impact factor: 5.293

Review 4.  Total Laboratory Automation for Rapid Detection and Identification of Microorganisms and Their Antimicrobial Resistance Profiles.

Authors:  Abdessalam Cherkaoui; Jacques Schrenzel
Journal:  Front Cell Infect Microbiol       Date:  2022-02-03       Impact factor: 5.293

Review 5.  Clinlabomics: leveraging clinical laboratory data by data mining strategies.

Authors:  Xiaoxia Wen; Ping Leng; Jiasi Wang; Guishu Yang; Ruiling Zu; Xiaojiong Jia; Kaijiong Zhang; Birga Anteneh Mengesha; Jian Huang; Dongsheng Wang; Huaichao Luo
Journal:  BMC Bioinformatics       Date:  2022-09-24       Impact factor: 3.307

  5 in total

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